Career Summary

Biography

Karen Blackmore received her BInfoTech(SpatialInfo) With Distinction in 2001 and her PhD in 2008 from Charles Sturt University, Australia. Her PhD research was cross-disciplinary in nature and focused on agent-based modelling of business strategies and their associated changing resource needs. Specifically, this work involved the use of data mining, clustering and visualisation to identify and explore patterns in a large longitudinal data set. Her postdoctoral research work was conducted at the University of Newcastle, in collaboration with Hunter Councils. This work focused on the use of self-organising maps and data analysis techniques to model the environmental impacts of climate change. This work was awarded LGSA’s Environment Award for Energy Saving and Climate Projection Winner C Division & Overall Category Winner 2009. She also has a research track record in the areas of business strategy modelling, data mining, information visualisation, pattern recognition, computer games and education.

Research ExpertiseMy major areas of research interest include: * Agent based models of complex adaptive systems * Application of data mining and pattern recognition techniques to understand patterns in global climate model data * Spatial and aspatial models of social and physical systems * Cross-disciplinary research issues My early research focus centred on data mining and spatial data modelling. For example, I explored the use of rule based classifiers, neural networks, genetic algorithms and fuzzy logic to find patterns in “Missing Persons Data” (Blackmore, et al. 2005; Blackmore & Bossomaier 2003a, 2003b; Blackmore & Bossomaier 2002a, 2002b; Blackmore et al. 2002). Data mining, clustering and statistical modelling also featured significantly in my PhD and Postdoctoral research. My PhD research involved modelling and analysing patterns associated with changing resource needs in organisations. A number of publications have arisen from this work (Blackmore et al. 2003; Blackmore & Nesbitt 2009; Blackmore & Nesbitt 2012). More recently, I have published results from my postdoctoral work that uses Self-Organising Maps (SOMS) and statistical downscaling to model regional climate variability (Goodwin, Freeman & Blackmore 2010; Goodwin & Blackmore forthcoming). In addition to the above academic publications, during 2008 to 2010 I was principal or co-author on eleven (11) reports (six allocated ISBN numbers) associated with my industry based postdoctoral studies and my employment with the University of Newcastle’s Centre for Urban and Regional Studies (CURS). The postdoctoral work was in collaboration with the Hunter Central Coast Regional Environment Strategy (HCCREMS). This work has been applied and used as the basis for the development of climate change adaptation strategies by local government authorities within the Hunter and Central Coast region. My work with CURS was conducted under an ARC Linkage grant and focussed on inter-agency data sharing and involved spatial data analysis of social vulnerability. The work was conducted in collaboration with the University of Western Sydney and the Department of Premier and Cabinet. Lastly, I have a research record and interest in areas relating to teaching and learning. I have investigated the complex factors associated with plagiarism in courses offered through partner or offshore campuses (Moffatt & Blackmore 2005, 2006) and issues in cross-disciplinary research higher degree research (Blackmore & Nesbitt 2008). In my role with Planning, Quality and Reporting at the University of Newcastle, I authored numerous research reports on a range of topics related to improving the student experience and developing strategies to improve the University’s performance in global ranking schemes. One of these reports formed the basis of a current University project aimed at reducing student attrition. Additionally, my work titled “Fuzzy Data Mining Approaches to Predicting Student Success and Retention” was presented at Australasian Association for Institutional Research Annual Forum held in November, 2012.

Teaching ExpertiseI have teaching experience at a University level in a range of IT areas. This experience encompasses different modes of delivery (eg. Internal and Distance Education) and ranges in level from 1st year to Masters and Graduate Certificate programs. I have delivered courses in the following areas:• Computer Games Production • Database Management Systems • Database Systems • Principles of Database Development • ICT Fundamentals • Managing Internet Marketing Information • Market Research • Geographic Information Systems • Digital Image Analysis • Strategic Information Management • Commerce on the Information Superhighway • Introduction to the Senses • Relationship Marketing • Introduction to Remote Sensing

I am committed to the delivery of high quality teaching and engage in continuing professional development activities (eg. Tertiary Teaching Colloquium and education research publications) to ensure my skills in this area are appropriate and relevant to the needs of students. The quality of my teaching has been evidenced through positive student and peer feedback, both in terms of the way I deal with students and the quality of the materials I develop to support my teaching.

Administrative ExpertiseI have been an active member of school based marketing and research committees, as well as being a member of Faculty level Marketing committees. My involvement in the marketing committees stems from expertise in this area and also an interest in making courses more attractive to, and reaching, potential students.

CollaborationsMacquarie University - Continued research building on from postdoctoral work to derive regional climate change projections. Research involves the use of self organising maps (SOMs) to produce synoptic types, statistical analysis of weather station data, statistical downscaling and rule based classification. Ongoing work focuses on spatial modelling of shoreline changes and analysis of complex global climate data.

This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a ... [more]

This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a Â¿bottom-upÂ¿ modelling paradigm in which system-level behaviour (macro) is modelled through the behaviour of individual local-level agent interactions (micro). This approach of considering the behaviour of systems of interacting Â¿agentsÂ¿ has been applied to a wide variety of domains. Of particular interest, are the ways that ABMS applications have been used to further understand the dynamics of the HE domain. We conduct a systematic review of literature to analyse publications by year, role of the simulator, development stage of the models, and any associated validation. We also identify areas for future work, which includes an emphasis on validating existing and future models, detailed description of simulations to allow replication and further development, and the use of agent-based models in other contexts within the increasingly complex HE domain.

Sound in video games is often used by developers to enhance the visual experience on screen. Despite its importance in creating presence and improving visual screen elements, soun... [more]

Sound in video games is often used by developers to enhance the visual experience on screen. Despite its importance in creating presence and improving visual screen elements, sound also plays an important role in providing additional information to a player when completing various game tasks. This preliminary study focuses on the use of informative sound in the popular multiplayer online battle arena game, Dota 2. Our initial results indicate that team performance improves with the use of sound. However, mixed results with individual performances were measured, with some individual performances better with sound and some better without sound.

Blackmore K, Bossomaier TRJ, 'Comparison of See5 and J48.PART Algorithms for Missing Persons Profiling', Proceedings of the First International Conference on Information Technology and Applications (ICITA 2002) (2002)

Algorithms to derive rules from data sets can obtain differing results from the same data set. The J48.PART and the See5 schemes use similar methodologies to derive rules, however... [more]

Algorithms to derive rules from data sets can obtain differing results from the same data set. The J48.PART and the See5 schemes use similar methodologies to derive rules, however, differences appear in the number and constitution of rules produced to predict outcomes for missing persons cases. See5 generates fewer rules to obtain the same level of accuracy as J48.PART. Analysis of the input-output space using a measure of concept variation indicates missing persons profiling is characteristic of a difficult classification problem, resulting in fragmentation problems. This provides explanation for the differences that occur in the number and constitution of rules.